Timezone: »
Learning the joint dependence of discrete variables is a fundamental problem in machine learning, with many applications including prediction, clustering and dimensionality reduction. More recently, the framework of copula modeling has gained popularity due to its modular parametrization of joint distributions. Among other properties, copulas provide a recipe for combining flexible models for univariate marginal distributions with parametric families suitable for potentially high dimensional dependence structures. More radically, the extended rank likelihood approach of Hoff (2007) bypasses learning marginal models completely when such information is ancillary to the learning task at hand as in, e.g., standard dimensionality reduction problems or copula parameter estimation. The main idea is to represent data by their observable rank statistics, ignoring any other information from the marginals. Inference is typically done in a Bayesian framework with Gaussian copulas, and it is complicated by the fact this implies sampling within a space where the number of constraints increase quadratically with the number of data points. The result is slow mixing when using off-the-shelf Gibbs sampling. We present an efficient algorithm based on recent advances on constrained Hamiltonian Markov chain Monte Carlo that is simple to implement and does not require paying for a quadratic cost in sample size.
Author Information
Alfredo Kalaitzis (University of Oxford)
Ricardo Silva (University College London)
More from the Same Authors
-
2021 : Single Image Super-Resolution with Uncertainty Estimation for Lunar Satellite Images »
Jose Delgado-Centeno · Paula Harder · Ben Moseley · Valentin Bickel · Siddha Ganju · Miguel Olivares · Alfredo Kalaitzis -
2021 : Single Image Super-Resolution with Uncertainty Estimation for Lunar Satellite Images »
Jose Delgado-Centeno · Paula Harder · Ben Moseley · Valentin Bickel · Siddha Ganju · Miguel Olivares · Alfredo Kalaitzis -
2022 : Pragmatic Fairness: Optimizing Policies with Outcome Disparity Control »
Limor Gultchin · Siyuan Guo · Alan Malek · Silvia Chiappa · Ricardo Silva -
2022 : Evaluating the Impact of Geometric and Statistical Skews on Out-Of-Distribution Generalization Performance »
Aengus Lynch · Jean Kaddour · Ricardo Silva -
2022 : Evaluating the Impact of Geometric and Statistical Skews on Out-Of-Distribution Generalization Performance »
Aengus Lynch · Jean Kaddour · Ricardo Silva -
2022 : Partial identification without distributional assumptions »
Kirtan Padh · Jakob Zeitler · David Watson · Matt Kusner · Ricardo Silva · Niki Kilbertus -
2022 Poster: When Do Flat Minima Optimizers Work? »
Jean Kaddour · Linqing Liu · Ricardo Silva · Matt Kusner -
2021 : Ricardo Silva - The Road to Causal Programming »
Ricardo Silva -
2021 Poster: Causal Effect Inference for Structured Treatments »
Jean Kaddour · Yuchen Zhu · Qi Liu · Matt Kusner · Ricardo Silva -
2020 : Invited Talk: On Prediction, Action and Interference »
Ricardo Silva -
2020 Poster: A Class of Algorithms for General Instrumental Variable Models »
Niki Kilbertus · Matt Kusner · Ricardo Silva -
2018 Poster: Bayesian Semi-supervised Learning with Graph Gaussian Processes »
Yin Cheng Ng · Nicolò Colombo · Ricardo Silva -
2017 Workshop: From 'What If?' To 'What Next?' : Causal Inference and Machine Learning for Intelligent Decision Making »
Ricardo Silva · Panagiotis Toulis · John Shawe-Taylor · Alexander Volfovsky · Thorsten Joachims · Lihong Li · Nathan Kallus · Adith Swaminathan -
2017 Poster: Counterfactual Fairness »
Matt Kusner · Joshua Loftus · Chris Russell · Ricardo Silva -
2017 Oral: Counterfactual Fairness »
Matt Kusner · Joshua Loftus · Chris Russell · Ricardo Silva -
2017 Poster: Tomography of the London Underground: a Scalable Model for Origin-Destination Data »
Nicolò Colombo · Ricardo Silva · Soong Moon Kang -
2017 Poster: When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness »
Chris Russell · Matt Kusner · Joshua Loftus · Ricardo Silva -
2016 Workshop: "What If?" Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems »
Ricardo Silva · John Shawe-Taylor · Adith Swaminathan · Thorsten Joachims -
2016 Poster: Observational-Interventional Priors for Dose-Response Learning »
Ricardo Silva -
2016 Poster: Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages »
Yin Cheng Ng · Pawel M Chilinski · Ricardo Silva -
2014 Poster: Causal Inference through a Witness Protection Program »
Ricardo Silva · Robin Evans -
2011 Poster: Thinning Measurement Models and Questionnaire Design »
Ricardo Silva -
2007 Poster: Hidden Common Cause Relations in Relational Learning »
Ricardo Silva · Wei Chu · Zoubin Ghahramani -
2007 Spotlight: Hidden Common Cause Relations in Relational Learning »
Ricardo Silva · Wei Chu · Zoubin Ghahramani